There are many situations where imagery data is collected for the purpose of detecting candidate objects or targets from collected background data. One of the more difficult aspects of object or target detection is to improve the detection of objects or targets while minimizing False Alarms. Processing and computing requirements for a given target detection performance are also highly variable. Obviously, it is desirable to improve target detection capability while minimizing processor load and likelihood of false detection. A number of target detection schemes have been used in the past.
Reis et al., U.S. Pat. No. 5,341,142, discloses a target acquisition and tracking system for a focal plane array seeker that uses maximum likelihood classification, the video spatial clustering and a target-to-interference ratio. The approach disclosed in Reis et al. primarily uses hierarchical edge based analysis and requires relatively intensive processing requiring relatively great processor speed for a given target detection performance.
Deaett et al., U.S. Pat. No. 6,072,889 discloses a system and method for imaging target detection by identifying image pixels of similar intensity levels, grouping contiguous pixels with similar intensity levels into regions, calculating a set of features for each region and qualifying regions as possible targets in response to the features. Deaett et al. then discriminates the background by identifying the image pixels of similar intensity levels into regions, calculating a set of features for each background region, qualifying background regions as terrain characteristics in response to the features and analyzing the qualified terrain characteristics and qualified target candidates to determine and prioritize targets. Simple pixel association at the gray level is performed to identify image pixels of similar intensity levels and grouping contiguous pixels with similar intensity into regions. Deaett et al. then uses feature extractors and classifiers to identify targets.